The intense competition between online publishers to acquire new customers and retain existing ones, coupled with a renewed interest in online advertising, has focused industry attention on the personalization of content and advertising. The goal of such personalization is to tailor the selection of online content and advertising to the interests of a particular user or group of users.
Content personalization is an application in the field of “adaptive computation”: the creation of computer programs that improve over time based on experience. Personalized content delivery is preferably adaptive because editors cannot hand-select content for each individual or small group, and because few consumers are willing to invest sufficient effort up front to thoroughly manually customize their own content. However, every user wants to be empowered to occasionally exert at least some control, and a small minority wants a lot of control. An effective content personalization solution should gracefully combine adaptive computation with an ability for users to directly see and modify the rules that are being used to select content for the, when they so choose.
Content personalization is difficult because each individual user has a unique set of reasons for preferring one content item over another. To accommodate each individual user's content preferences, a computer program should be able to encompass a wide range of varied rules for selecting content, such as a content item's source or author, the topics that it covers, its style of writing, the content item's popularity among other users, and the like. These varied rules for selecting content must be combined in a flexible way that gives each individual user or group a personal algorithm for delivering content.
In recent years, a wide variety of technical approaches to this problem have been taken. These approaches have ranged from traditional collaborative filtering (such as the product recommendations on Amazon.com) to adaptive computation techniques such as neural nets and genetic algorithms. However, none of these personalization efforts have succeeded in effectively blending the necessary ingredients: adaptive computation, empowering the user to see and modify the rules, and encompassing a wide range of varied rules into a personal algorithm for each user or group.
The present invention overcomes these limitations and deficiencies in the prior art by providing methods and systems for learning rule sets for personalized search and filtering as described herein.